Why might Mean Absolute Error (MAE) sometimes be preferred over Mean Absolute Percentage Error (MAPE) when measuring forecast success?
Answer
Large errors during small demand periods can skew MAPE results disproportionately
MAE is often preferred in certain energy contexts because MAPE's percentage calculation can be overly sensitive and disproportionately penalized by large errors occurring when overall demand is low.
Related Questions
What is the primary objective in energy forecasting that directly influences cost and risk?What consequence arises when a power generator under-forecasts electricity demand?Which external factor is cited as almost always the single most influential driver of energy load?What do classical statistical forecasting methods like ARIMA typically struggle with in modern energy systems?Which model types are often preferred for predicting load one to four hours ahead due to their ability to capture temporal dependencies?Which operational decisions are Long-Term Forecasting (LTF) projections primarily essential for?According to sector professionals, what percentage of project time can data preparation, cleaning, and feature engineering consume?What is the term for the decline in model accuracy over time caused by real-world changes not present in the training data?Why might Mean Absolute Error (MAE) sometimes be preferred over Mean Absolute Percentage Error (MAPE) when measuring forecast success?What central trade-off remains a major theme in the practical implementation of energy forecasting models?